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A Science of Natural Open Systems
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Using the conservation of change to discover the divergent & convergent workings of change! --
05/08 05/08 05/08 07/20/08 9/17/08 The main question in science and philosophy has always been why so many things seem to have organization and behaviors of their own, appearing of local creative design as if having 'free will' where there seems to be no reason for it at all. It conflicts with our cultural heritage of belief in determinism, and so does more and more of what we're finding about the design of actual physical things and processes My work is about having found an efficient way to watch closely as things exhibit that behavior. In the end I think it both elevates the meaning and importance of the ordinary as well as of the human drama. As Stuart Kauffman recently observed, the clear appearance is that our familiar view of the ordinary is truly impoverished. We have much to learn.
I've had the very unusual privilege of finding a fascinating large gap in the scientific method and an intriguing good way to fill it. It points toward returning science to being a general purpose tool for anyone with curiosity, a general craft rather than a single specialty. [For the layman, 'conservation' refers to properties that are sustained, it's what makes adding and subtracting them work, and so all of science. This is then about solving the riddle of how that conserved change in nature comes and goes.] Natural systems producing conserved change can be identified by the emergence of continuity in their development, i.e. periods with observable derivative rates of change .
That evidence can be found in the development of most observable events, and in all natural and man made economic systems. The main trick is that the continuities in natural systems move around, so wherever you look they seem intermittent. Putting together how emerging processes develop starts with observing conserved change.
What you find, even when outcomes are predictable, is that much less is strictly pre-determined than you might think. The development of individual events and systems follows a path of local development in the local environment. Watching closely how it happens can help inform our models. It can not be modeled itself because it is local organizational development involving many independently reacting parts, a learning process. Inter-dependent controls can't emulate inter-dependent discoveries. Events can be seen to emerge from local growth processes starting from a 'seed' of original opportunity and design.
What develops is a conserved 'little bang' of explosive change, that points to the network of relationships that is emerging. You can watch them as they find their own paths,... and then run into each other. It's an organization making rather than organization following process. The basic technique is to look for the beginning and end of continuities of change, and look closer to what connects them. Continuities of change identify local systems that "connect the dots". Browse around and think about emergent events in your own world, then write with good questions. pfh
Modern science developed around the model of physics, containing a major limitation: physics did not include a method for studying individual events. Individual events turn out to have original individual development paths that confound traditional formulas and statistical description. To represent them physics would need to represent their environments, before those environments are explored. Unable to represent environments either before or after they are explored, the traditional physics method uses only the statistical variation for collections different individual events to represent their environments as an uncertain boundary condition constant.
Environments, though, are neither uncertain nor constant, but very definite and changing. This is a big gap to fill, so I developed a new physics of change to fill the gap by asking a different kind of question, creating a way to study individual events as a learning process for what people are directly observing. In just the same way as traditional physics you take the learning process to a point of diminishing returns, just following a new kind of question and producing a new kind of highly useful result. The main problem has not been a lack of data. It's been that we ask the wrong kinds of questions about the plentiful kinds of naturally available data.
The largest benefit comes when you can recognize in events or systems an accumulative development, design or learning processes. For physics, that means that some novel local feature is being 'conserved', the conservation laws locally apply, and you can define meaningful derivative rates of change. For engineering and design, it directs your attention to the path-finding behavior that records an accumulative 'exploration' of environmental opportunities and the paths that connect them. For philosophers, it indicates where there is a world beyond the world known to any individual. Representing what is known omits the process of exploring the unknown by which the known was discovered, representing how we open our minds seemingly only to close them more tightly. Environments and natural systems could be represented another way, with keys to asking the right questions as you explore them...
System development is like a storm that draws 'energy' from where it is found. Wherever new paths are developing, a model of the past directions taken will not show the paths to be taken in the future, but could be read to look for them. There turn out to be various long range forecasting signs to help, but if you just learn to ask the right questions, and watch closely, you'll see the new directions being taken much sooner than someone that does not. You start from common informal impressions of change, in commonly observable physical, social or emotional systems, and using your own careful learning to develop a sensitive navigation instrument.
Accumulative original path-making seems to be part of nearly all individual events of interest, and so common experience already gives us a lot of (perhaps disorganized) knowledge about them. What I developed is a systematic way of asking better questions about how independent systems develop their own learning paths. You can potentially use it to combine some of your own large store of common knowledge and direct observation to become much more meaningful. Some of the key questions are very general, but their long range forecasting potential is huge. The four basic questions are to a) what observations suggest 'arrows' of change? b) how are the directions they point changing? c) what processes are doing that and what paths are they finding in the environment in which they're moving?, and d) what will they run into and how will that change them?
Common experience gives us lots of usable models for this, once you get the idea of watching nature's learning paths at work! [Theoretical note: A 'model' is a 'metaphor', a mental construct to help stimulate the imagination for ways to directly engage with the world. Scientific models get their value not from being the same as natural processes, but as 'metaphors' for nature that engineers and designers use in the process of directly learning how to make and do things. Models are nothing at all without the people using them to for relating to things beyond explanation. They're 'guides'.]
There start is to use some simple examples of natural systems as a model. I seem always far behind on my edits, but my 'bump on a curve' method discusses a few that can be generalized to help you learn about all natural systems. 'Generalizing' them provides places for ordinary observations to plug in and then begin to track the paths a whole system and its learning process are following. One kind we all pay great attention to is watching the learning paths that new personal relationships take. It starts at our first contact with a new friend or new relationship at work and we naturally look for the 'arrows' that point where it might go. 'Arrows' are changes that point to a direction of change, like how readily they respond, or if the kinds of responses branch out or not.
If those arrows start small and change by steadily bigger steps, then you might say the relationship is 'taking off'. This gives a very concrete meaning to build on the general sense of what 'taking off' means informally. Then you begin seeing how the other person's responses and the relationship's directions reflect things being discovered in the environment through which the relationship is making its path. That's a major step, toward having it become 'real' and participate in the world.
Then there are hazards, like not watching as the relationship alters its own environment, or something else does, and you're not ready to be responsive when you need to be.., or then unable to understand what happened. If successful, relationships go from 'start' through 'take off' to 'integration', if they avoid embarrassing failure along the way. For each learning process as a whole the development always goes from first beginning to complete end, and always experiences the four basic changes in direction, in one or another form. ¸¸¸.·´ ¯ `·.¸¸¸ This way of organizing the historical progression of personal relationship issues is just one good way to start thinking of about natural systems as complex relationships that follow a learning process.
This can be defined in rigorous scientific terms or left informal, applied to help understand familiar short or long term learning experiences, as well as ones that take a thousand years to develop like a civilization or a few nanoseconds like the plasma cascade that forms the learning path of a spark or lightning. All development paths start with an explosion by a process that is then altered by what that explosion runs into. pfh
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